Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908787823083520 |
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| author | Huang, Weixiong Wang, Rui Li, Wenhua Qi, Sheng Luo, Tianyu Chen, Delong Zhang, Tao Wang, Ling |
| author_facet | Huang, Weixiong Wang, Rui Li, Wenhua Qi, Sheng Luo, Tianyu Chen, Delong Zhang, Tao Wang, Ling |
| contents | Real world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained multiobjective evolutionary algorithms (CMOEAs) suffer from a number of drawbacks, including complex designs, low computational efficiency, and long convergence times, which are particularly pronounced when addressing time-sensitive CMOPs. Although research on accelerating evolutionary algorithms using GPU parallelism has advanced, existing CMOEAs still face significant limitations within GPU frameworks. To overcome these challenges, this paper proposes a GPU-accelerated multi-population evolutionary algorithm, termed GMPEA. We first systematically analyze the performance bottlenecks of representative CMOEAs when implemented in a GPU environment. To address the trade-off between computational speed and solution performance, GMPEA introduces a decomposition-based multi-population approach that is fully parallelized across its entire workflow. We conducted comparative experiments on various benchmark tests and real world applications: the Weapon Target Assignment Problems. The results demonstrate that GMPEA achieves competitive performance even without time constraints, while its computational speed significantly surpasses that of the compared algorithms. More critically, under a strict time limit, the performance of GMPEA drastically outperforms its counterparts. This work provides compelling evidence of GMPEA's superiority in solving time-sensitive CMOPs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_19821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems Huang, Weixiong Wang, Rui Li, Wenhua Qi, Sheng Luo, Tianyu Chen, Delong Zhang, Tao Wang, Ling Neural and Evolutionary Computing Real world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained multiobjective evolutionary algorithms (CMOEAs) suffer from a number of drawbacks, including complex designs, low computational efficiency, and long convergence times, which are particularly pronounced when addressing time-sensitive CMOPs. Although research on accelerating evolutionary algorithms using GPU parallelism has advanced, existing CMOEAs still face significant limitations within GPU frameworks. To overcome these challenges, this paper proposes a GPU-accelerated multi-population evolutionary algorithm, termed GMPEA. We first systematically analyze the performance bottlenecks of representative CMOEAs when implemented in a GPU environment. To address the trade-off between computational speed and solution performance, GMPEA introduces a decomposition-based multi-population approach that is fully parallelized across its entire workflow. We conducted comparative experiments on various benchmark tests and real world applications: the Weapon Target Assignment Problems. The results demonstrate that GMPEA achieves competitive performance even without time constraints, while its computational speed significantly surpasses that of the compared algorithms. More critically, under a strict time limit, the performance of GMPEA drastically outperforms its counterparts. This work provides compelling evidence of GMPEA's superiority in solving time-sensitive CMOPs. |
| title | Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2509.19821 |